SlideShare a Scribd company logo
1 of 31
Download to read offline
BIG DATA

Prepared By
Nasrin Irshad Hussain And Pranjal Saikia
M.Sc(IT) 2nd Sem
Kaziranga University Assam
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.

• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
What is BIG DATA?
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture

• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.

• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s

Volume

Velocity

Variety

• Data
quantity

• Data
Speed

• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in realtime
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.

• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data structure.
• Big Data analysis includes different types of data
Storing Big Data
Analyzing your data characteristics
• Selecting data sources for analysis
• Eliminating redundant data
• Establishing the role of NoSQL

Overview of Big Data stores
• Data models: key value, graph, document,
column-family
• Hadoop Distributed File System
• HBase
• Hive
Selecting Big Data stores
• Choosing the correct data stores based on
your data characteristics
• Moving code to data
• Implementing polyglot data store solutions

• Aligning business goals to the appropriate
data store
Processing Big Data
 Integrating disparate data stores
• Mapping data to the programming framework
• Connecting and extracting data from storage
• Transforming data for processing
• Subdividing data in preparation for Hadoop
MapReduce
 Employing Hadoop MapReduce
• Creating the components of Hadoop MapReduce jobs
• Distributing data processing across server farms
• Executing Hadoop MapReduce jobs
• Monitoring the progress of job flows
The Structure of Big Data
Structured
• Most traditional data
sources

Semi-structured
• Many sources of big
data

Unstructured
• Video data, audio data
13
Why Big Data
• Growth of Big Data is needed
– Increase of storage capacities

– Increase of processing power
– Availability of data(different data types)

– Every day we create 2.5 quintillion bytes of data;
90% of the data in the world today has been created
in the last two years alone
Why Big Data
•FB generates 10TB daily

•Twitter generates 7TB of data
Daily
•IBM claims 90% of today’s
stored data was generated
in just the last two years.
How Is Big Data Different?
1) Automatically generated by a machine
(e.g. Sensor embedded in an engine)
2) Typically an entirely new source of data
(e.g. Use of the internet)
3) Not designed to be friendly
(e.g. Text streams)
4) May not have much values
• Need to focus on the important part

16
Big Data sources
Users
Application

Systems
Sensors

Large and growing files
(Big data files)
Data generation points Examples
Mobile Devices
Microphones
Readers/Scanners
Science facilities
Programs/ Software
Social Media
Cameras
Big Data Analytics
• Examining large amount of data

• Appropriate information
• Identification of hidden patterns, unknown correlations
• Competitive advantage
• Better business decisions: strategic and operational
• Effective marketing, customer satisfaction, increased
revenue
Types of tools used in
Big-Data
• Where processing is hosted?
– Distributed Servers / Cloud (e.g. Amazon EC2)

• Where data is stored?
– Distributed Storage (e.g. Amazon S3)

• What is the programming model?
– Distributed Processing (e.g. MapReduce)

• How data is stored & indexed?
– High-performance schema-free databases (e.g. MongoDB)

• What operations are performed on data?
– Analytic / Semantic Processing
A Application Of Big Data analytics
Smarter
Healthcare

Homeland
Security

Traffic Control

Manufacturing

Multi-channel
sales

Telecom

Trading
Analytics

Search
Quality
Risks of Big Data
• Will be so overwhelmed
• Need the right people and solve the right problems

• Costs escalate too fast
• Isn’t necessary to capture 100%

• Many sources of big data
is privacy
• self-regulation
• Legal regulation
22
Leading Technology Vendors
Example Vendors
• IBM – Netezza
• EMC – Greenplum
• Oracle – Exadata

Commonality

• MPP architectures
• Commodity Hardware
• RDBMS based
• Full SQL compliance
How Big data impacts on IT
• Big data is a troublesome force presenting
opportunities with challenges to IT organizations.
• By 2015 4.4 million IT jobs in Big Data ; 1.9 million
is in US itself
• India will require a minimum of 1 lakh data
scientists in the next couple of years in addition
to data analysts and data managers to support
the Big Data space.
Potential Value of Big Data
• $300 billion potential annual
value to US health care.
• $600 billion potential annual
consumer surplus from using
personal location data.
• 60% potential in retailers’
operating margins.
India – Big Data
• Gaining attraction

• Huge market opportunities for IT services
(82.9% of revenues) and analytics firms
(17.1 % )
• Current market size is $200 million. By 2015 $1
billion
• The opportunity for Indian service providers lies
in offering services around Big Data
implementation and analytics for global
multinationals
Benefits of Big Data
•Real-time big data isn’t just a process for storing
petabytes or exabytes of data in a data warehouse, It’s
about the ability to make better decisions and take
meaningful actions at the right time.
•Fast forward to the present and technologies like Hadoop
give you the scale and flexibility to store data before you
know how you are going to process it.
•Technologies such as MapReduce,Hive and Impala enable
you to run queries without changing the data structures
underneath.
Benefits of Big Data
• Our newest research finds that organizations are using big
data to target customer-centric outcomes, tap into internal
data and build a better information ecosystem.
• Big Data is already an important part of the $64 billion
database and data analytics market
• It offers commercial opportunities of a comparable
scale to enterprise software in the late 1980s

• And the Internet boom of the 1990s, and the social media
explosion of today.
Future of Big Data
• $15 billion on software firms only specializing in
data management and analytics.
• This industry on its own is worth more than $100
billion and growing at almost 10% a year which is
roughly twice as fast as the software business as a
whole.
• In February 2012, the open source analyst firm
Wikibon released the first market forecast for Big
Data , listing $5.1B revenue in 2012 with growth to
$53.4B in 2017
• The McKinsey Global Institute estimates that data
volume is growing 40% per year, and will grow 44x
between 2009 and 2020.
References
• www.Slideshare.com
• www.wikipedia.com
• www.computereducation.org
• BooksBig Data by Viktor Mayer-Schonberger
Thank You.

More Related Content

What's hot (20)

Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
Big Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must KnowBig Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must Know
 
Introduction to Data Analytics
Introduction to Data AnalyticsIntroduction to Data Analytics
Introduction to Data Analytics
 
Business intelligence
Business intelligenceBusiness intelligence
Business intelligence
 
Big data
Big dataBig data
Big data
 
Big data lecture notes
Big data lecture notesBig data lecture notes
Big data lecture notes
 
Ppt for Application of big data
Ppt for Application of big dataPpt for Application of big data
Ppt for Application of big data
 
Big data
Big dataBig data
Big data
 
Data analytics
Data analyticsData analytics
Data analytics
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Introduction to Data Mining
Introduction to Data MiningIntroduction to Data Mining
Introduction to Data Mining
 
Data visualization
Data visualizationData visualization
Data visualization
 
Big Data PPT by Rohit Dubey
Big Data PPT by Rohit DubeyBig Data PPT by Rohit Dubey
Big Data PPT by Rohit Dubey
 
Data Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture NotesData Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture Notes
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
 
Big Data
Big DataBig Data
Big Data
 
Big Data Analytics
Big Data AnalyticsBig Data Analytics
Big Data Analytics
 
NOSQL- Presentation on NoSQL
NOSQL- Presentation on NoSQLNOSQL- Presentation on NoSQL
NOSQL- Presentation on NoSQL
 
HADOOP TECHNOLOGY ppt
HADOOP  TECHNOLOGY pptHADOOP  TECHNOLOGY ppt
HADOOP TECHNOLOGY ppt
 
The Evolution of Data Science
The Evolution of Data ScienceThe Evolution of Data Science
The Evolution of Data Science
 

Viewers also liked

Big Data
Big DataBig Data
Big DataNGDATA
 
Big Data - 25 Amazing Facts Everyone Should Know
Big Data - 25 Amazing Facts Everyone Should KnowBig Data - 25 Amazing Facts Everyone Should Know
Big Data - 25 Amazing Facts Everyone Should KnowBernard Marr
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with HadoopPhilippe Julio
 
Introduction to Big Data
Introduction to Big Data Introduction to Big Data
Introduction to Big Data Srinath Perera
 
Using Big Data for Improved Healthcare Operations and Analytics
Using Big Data for Improved Healthcare Operations and AnalyticsUsing Big Data for Improved Healthcare Operations and Analytics
Using Big Data for Improved Healthcare Operations and AnalyticsPerficient, Inc.
 
Big data analysis using map/reduce
Big data analysis using map/reduceBig data analysis using map/reduce
Big data analysis using map/reduceRenuSuren
 
Big-Data in HealthCare _ Overview
Big-Data in HealthCare _ OverviewBig-Data in HealthCare _ Overview
Big-Data in HealthCare _ OverviewHamdaoui Younes
 
mlsb07_zhao_iob.ppt
mlsb07_zhao_iob.pptmlsb07_zhao_iob.ppt
mlsb07_zhao_iob.pptbutest
 
Big implications of Big Data in healthcare
Big implications of Big Data in healthcareBig implications of Big Data in healthcare
Big implications of Big Data in healthcareGuires
 
How different between Big Data, Business Intelligence and Analytics ?
How different between Big Data, Business Intelligence and Analytics ?How different between Big Data, Business Intelligence and Analytics ?
How different between Big Data, Business Intelligence and Analytics ?Thanakrit Lersmethasakul
 
Bigdata presentation
Bigdata presentationBigdata presentation
Bigdata presentationYonas Gidey
 
CrystalBall - Compute Relative Frequency in Hadoop
CrystalBall - Compute Relative Frequency in Hadoop CrystalBall - Compute Relative Frequency in Hadoop
CrystalBall - Compute Relative Frequency in Hadoop Suvash Shah
 
Plotting Frequency Distributions
Plotting Frequency DistributionsPlotting Frequency Distributions
Plotting Frequency DistributionsMike Slater
 
Big data hadoop titles 2015 2016
Big data hadoop titles 2015 2016Big data hadoop titles 2015 2016
Big data hadoop titles 2015 2016xtreamtechnologies
 

Viewers also liked (20)

What is big data?
What is big data?What is big data?
What is big data?
 
Big Data
Big DataBig Data
Big Data
 
Big Data - 25 Amazing Facts Everyone Should Know
Big Data - 25 Amazing Facts Everyone Should KnowBig Data - 25 Amazing Facts Everyone Should Know
Big Data - 25 Amazing Facts Everyone Should Know
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with Hadoop
 
Big data ppt
Big data pptBig data ppt
Big data ppt
 
Introduction to Big Data
Introduction to Big Data Introduction to Big Data
Introduction to Big Data
 
Using Big Data for Improved Healthcare Operations and Analytics
Using Big Data for Improved Healthcare Operations and AnalyticsUsing Big Data for Improved Healthcare Operations and Analytics
Using Big Data for Improved Healthcare Operations and Analytics
 
Big Data: Issues and Challenges
Big Data: Issues and ChallengesBig Data: Issues and Challenges
Big Data: Issues and Challenges
 
Big data analysis using map/reduce
Big data analysis using map/reduceBig data analysis using map/reduce
Big data analysis using map/reduce
 
Big-Data in HealthCare _ Overview
Big-Data in HealthCare _ OverviewBig-Data in HealthCare _ Overview
Big-Data in HealthCare _ Overview
 
Big data ppt
Big data pptBig data ppt
Big data ppt
 
mlsb07_zhao_iob.ppt
mlsb07_zhao_iob.pptmlsb07_zhao_iob.ppt
mlsb07_zhao_iob.ppt
 
Big implications of Big Data in healthcare
Big implications of Big Data in healthcareBig implications of Big Data in healthcare
Big implications of Big Data in healthcare
 
How different between Big Data, Business Intelligence and Analytics ?
How different between Big Data, Business Intelligence and Analytics ?How different between Big Data, Business Intelligence and Analytics ?
How different between Big Data, Business Intelligence and Analytics ?
 
Bigdata presentation
Bigdata presentationBigdata presentation
Bigdata presentation
 
CrystalBall - Compute Relative Frequency in Hadoop
CrystalBall - Compute Relative Frequency in Hadoop CrystalBall - Compute Relative Frequency in Hadoop
CrystalBall - Compute Relative Frequency in Hadoop
 
Big Data Presentation
Big  Data PresentationBig  Data Presentation
Big Data Presentation
 
Plotting Frequency Distributions
Plotting Frequency DistributionsPlotting Frequency Distributions
Plotting Frequency Distributions
 
trng seminar
trng seminartrng seminar
trng seminar
 
Big data hadoop titles 2015 2016
Big data hadoop titles 2015 2016Big data hadoop titles 2015 2016
Big data hadoop titles 2015 2016
 

Similar to Big data ppt (20)

Big_Data_ppt[1] (1).pptx
Big_Data_ppt[1] (1).pptxBig_Data_ppt[1] (1).pptx
Big_Data_ppt[1] (1).pptx
 
Special issues on big data
Special issues on big dataSpecial issues on big data
Special issues on big data
 
ppt final.pptx
ppt final.pptxppt final.pptx
ppt final.pptx
 
Content1. Introduction2. What is Big Data3. Characte.docx
Content1. Introduction2. What is Big Data3. Characte.docxContent1. Introduction2. What is Big Data3. Characte.docx
Content1. Introduction2. What is Big Data3. Characte.docx
 
Kartikey tripathi
Kartikey tripathiKartikey tripathi
Kartikey tripathi
 
Big data ppt
Big data pptBig data ppt
Big data ppt
 
big-data-8722-m8RQ3h1.pptx
big-data-8722-m8RQ3h1.pptxbig-data-8722-m8RQ3h1.pptx
big-data-8722-m8RQ3h1.pptx
 
Big Data ppt
Big Data pptBig Data ppt
Big Data ppt
 
Presentation on Big Data
Presentation on Big DataPresentation on Big Data
Presentation on Big Data
 
Big data
Big dataBig data
Big data
 
BIGDATAPrepared ByMuhammad Abrar UddinIntrodu.docx
BIGDATAPrepared ByMuhammad Abrar UddinIntrodu.docxBIGDATAPrepared ByMuhammad Abrar UddinIntrodu.docx
BIGDATAPrepared ByMuhammad Abrar UddinIntrodu.docx
 
Bigdata
BigdataBigdata
Bigdata
 
bigdata.pptx
bigdata.pptxbigdata.pptx
bigdata.pptx
 
Big data and analytics
Big data and analyticsBig data and analytics
Big data and analytics
 
Big Data
Big DataBig Data
Big Data
 
Big data Analytics
Big data Analytics Big data Analytics
Big data Analytics
 
Bigdata " new level"
Bigdata " new level"Bigdata " new level"
Bigdata " new level"
 
Big data
Big dataBig data
Big data
 
bigdatappt.pptx
bigdatappt.pptxbigdatappt.pptx
bigdatappt.pptx
 
Big data Presentation
Big data PresentationBig data Presentation
Big data Presentation
 

Big data ppt

  • 1. BIG DATA Prepared By Nasrin Irshad Hussain And Pranjal Saikia M.Sc(IT) 2nd Sem Kaziranga University Assam
  • 2. Content 1. Introduction 2. What is Big Data 3. Characteristic of Big Data 4. Storing,selecting and processing of Big Data 5. Why Big Data 6. How it is Different 7. Big Data sources 8. Tools used in Big Data 9. Application of Big Data 10. Risks of Big Data 11. Benefits of Big Data 12. How Big Data Impact on IT 13. Future of Big Data
  • 3. Introduction • Big Data may well be the Next Big Thing in the IT world. • Big data burst upon the scene in the first decade of the 21st century. • The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning. • Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
  • 4. What is BIG DATA? • ‘Big Data’ is similar to ‘small data’, but bigger in size • but having data bigger it requires different approaches: – Techniques, tools and architecture • an aim to solve new problems or old problems in a better way • Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques.
  • 5. What is BIG DATA • Walmart handles more than 1 million customer transactions every hour. • Facebook handles 40 billion photos from its user base. • Decoding the human genome originally took 10years to process; now it can be achieved in one week.
  • 6. Three Characteristics of Big Data V3s Volume Velocity Variety • Data quantity • Data Speed • Data Types
  • 7. 1st Character of Big Data Volume •A typical PC might have had 10 gigabytes of storage in 2000. •Today, Facebook ingests 500 terabytes of new data every day. •Boeing 737 will generate 240 terabytes of flight data during a single flight across the US. • The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
  • 8. 2nd Character of Big Data Velocity • Clickstreams and ad impressions capture user behavior at millions of events per second • high-frequency stock trading algorithms reflect market changes within microseconds • machine to machine processes exchange data between billions of devices • infrastructure and sensors generate massive log data in realtime • on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
  • 9. 3rd Character of Big Data Variety • Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media. • Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure. • Big Data analysis includes different types of data
  • 10. Storing Big Data Analyzing your data characteristics • Selecting data sources for analysis • Eliminating redundant data • Establishing the role of NoSQL Overview of Big Data stores • Data models: key value, graph, document, column-family • Hadoop Distributed File System • HBase • Hive
  • 11. Selecting Big Data stores • Choosing the correct data stores based on your data characteristics • Moving code to data • Implementing polyglot data store solutions • Aligning business goals to the appropriate data store
  • 12. Processing Big Data  Integrating disparate data stores • Mapping data to the programming framework • Connecting and extracting data from storage • Transforming data for processing • Subdividing data in preparation for Hadoop MapReduce  Employing Hadoop MapReduce • Creating the components of Hadoop MapReduce jobs • Distributing data processing across server farms • Executing Hadoop MapReduce jobs • Monitoring the progress of job flows
  • 13. The Structure of Big Data Structured • Most traditional data sources Semi-structured • Many sources of big data Unstructured • Video data, audio data 13
  • 14. Why Big Data • Growth of Big Data is needed – Increase of storage capacities – Increase of processing power – Availability of data(different data types) – Every day we create 2.5 quintillion bytes of data; 90% of the data in the world today has been created in the last two years alone
  • 15. Why Big Data •FB generates 10TB daily •Twitter generates 7TB of data Daily •IBM claims 90% of today’s stored data was generated in just the last two years.
  • 16. How Is Big Data Different? 1) Automatically generated by a machine (e.g. Sensor embedded in an engine) 2) Typically an entirely new source of data (e.g. Use of the internet) 3) Not designed to be friendly (e.g. Text streams) 4) May not have much values • Need to focus on the important part 16
  • 17. Big Data sources Users Application Systems Sensors Large and growing files (Big data files)
  • 18. Data generation points Examples Mobile Devices Microphones Readers/Scanners Science facilities Programs/ Software Social Media Cameras
  • 19. Big Data Analytics • Examining large amount of data • Appropriate information • Identification of hidden patterns, unknown correlations • Competitive advantage • Better business decisions: strategic and operational • Effective marketing, customer satisfaction, increased revenue
  • 20. Types of tools used in Big-Data • Where processing is hosted? – Distributed Servers / Cloud (e.g. Amazon EC2) • Where data is stored? – Distributed Storage (e.g. Amazon S3) • What is the programming model? – Distributed Processing (e.g. MapReduce) • How data is stored & indexed? – High-performance schema-free databases (e.g. MongoDB) • What operations are performed on data? – Analytic / Semantic Processing
  • 21. A Application Of Big Data analytics Smarter Healthcare Homeland Security Traffic Control Manufacturing Multi-channel sales Telecom Trading Analytics Search Quality
  • 22. Risks of Big Data • Will be so overwhelmed • Need the right people and solve the right problems • Costs escalate too fast • Isn’t necessary to capture 100% • Many sources of big data is privacy • self-regulation • Legal regulation 22
  • 23. Leading Technology Vendors Example Vendors • IBM – Netezza • EMC – Greenplum • Oracle – Exadata Commonality • MPP architectures • Commodity Hardware • RDBMS based • Full SQL compliance
  • 24. How Big data impacts on IT • Big data is a troublesome force presenting opportunities with challenges to IT organizations. • By 2015 4.4 million IT jobs in Big Data ; 1.9 million is in US itself • India will require a minimum of 1 lakh data scientists in the next couple of years in addition to data analysts and data managers to support the Big Data space.
  • 25. Potential Value of Big Data • $300 billion potential annual value to US health care. • $600 billion potential annual consumer surplus from using personal location data. • 60% potential in retailers’ operating margins.
  • 26. India – Big Data • Gaining attraction • Huge market opportunities for IT services (82.9% of revenues) and analytics firms (17.1 % ) • Current market size is $200 million. By 2015 $1 billion • The opportunity for Indian service providers lies in offering services around Big Data implementation and analytics for global multinationals
  • 27. Benefits of Big Data •Real-time big data isn’t just a process for storing petabytes or exabytes of data in a data warehouse, It’s about the ability to make better decisions and take meaningful actions at the right time. •Fast forward to the present and technologies like Hadoop give you the scale and flexibility to store data before you know how you are going to process it. •Technologies such as MapReduce,Hive and Impala enable you to run queries without changing the data structures underneath.
  • 28. Benefits of Big Data • Our newest research finds that organizations are using big data to target customer-centric outcomes, tap into internal data and build a better information ecosystem. • Big Data is already an important part of the $64 billion database and data analytics market • It offers commercial opportunities of a comparable scale to enterprise software in the late 1980s • And the Internet boom of the 1990s, and the social media explosion of today.
  • 29. Future of Big Data • $15 billion on software firms only specializing in data management and analytics. • This industry on its own is worth more than $100 billion and growing at almost 10% a year which is roughly twice as fast as the software business as a whole. • In February 2012, the open source analyst firm Wikibon released the first market forecast for Big Data , listing $5.1B revenue in 2012 with growth to $53.4B in 2017 • The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020.
  • 30. References • www.Slideshare.com • www.wikipedia.com • www.computereducation.org • BooksBig Data by Viktor Mayer-Schonberger

Editor's Notes

  1. Acco.to IBM